DocumentCode :
1644057
Title :
Assessment of osteoarthritis severity by wavelet analysis of the hip joint space radial distance signature
Author :
Boniatis, Joannis ; Panagiotopoulos, Elias ; Lymberopoulos, Dimitrios ; Panayiotakis, George
Author_Institution :
Dept. of Med. Phys., Univ. of Patras, Rio
fYear :
2008
Firstpage :
1
Lastpage :
6
Abstract :
Osteoarthritis (OA) is a major cause of morbidity worldwide, representing the most common form of arthritis. The radiographic assessment of OA-severity is mainly relied on qualitative criteria, evaluating structural alterations of the joint. In the present study a computer-based image analysis method was developed for the grading of hip OA-severity from radiographic images. The sample of the study comprised 64 hips (18 normal, 46 osteoarthritic), corresponding to 32 unilateral and bilateral hip-OA patients. Two experienced orthopaedists assessed OA-severity from pelvic radiographs, employing the Kellgren and Lawrence (KL) grading scale. Accordingly, 3 KL-based OA-severity categories were formed: (i) ldquonormal/doubtfulrdquo, (ii) ldquomild/moderaterdquo, and ldquosevererdquo. After radiographs digitization their contrast was enhanced by means of the contrast limited adaptive histogram equalization method. Employing custom developed algorithms: (i) 64 ROIs, corresponding to patientspsila hip joint spaces (HJSs), were determined on the processed radiographs, and (ii) the radial distance signature (RDS) of each HJS-ROI was generated, as the sequence of the Euclidean radial distances between the ldquocentre of mass (centroid)rdquo and each point of the HJS-ROI contour. The generated RDS was subject to the discrete wavelet transform (Coiflet1 wavelet, Level 2 decomposition). Statistical measures of the generated wavelet coefficients were used for the formation of feature vectors, representative of the HJS-ROIs. These vectors were involved in the design of a grading system, based on the Bayes classifier, which was used for the discrimination between: (i) normal and OA hips, and (ii) hips of ldquoMild / Moderaterdquo and ldquoSevererdquo OA. The classification accuracy achieved regarding the discrimination between normal and OA hips was 95.3%, while the relevant score for the characterization of hips as of ldquomild/moderaterdquo or ldquosevererdquo OA was 91.3%. The prop- - osed system could be of value for the management of hip OA patient.
Keywords :
Bayes methods; diagnostic radiography; diseases; feature extraction; image classification; image enhancement; medical image processing; orthopaedics; wavelet transforms; Bayes classifier; Euclidean radial distances; Kellgren grading scale; Lawrence grading scale; bilateral patients; computer-based image analysis method; contrast enhancement; contrast limited adaptive histogram equalization; discrete wavelet transform; feature vectors; hip joint space radial distance signature; joint. structural alterations; osteoarthritis severity assessment; pelvic radiographs; radial distance signature; radiograph digitization; radiographic assessment; unilateral patients; wavelet coefficients; Adaptive equalizers; Arthritis; Diagnostic radiography; Discrete wavelet transforms; Hip; Histograms; Image sequence analysis; Osteoarthritis; Wavelet analysis; Wavelet coefficients;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
BioInformatics and BioEngineering, 2008. BIBE 2008. 8th IEEE International Conference on
Conference_Location :
Athens
Print_ISBN :
978-1-4244-2844-1
Electronic_ISBN :
978-1-4244-2845-8
Type :
conf
DOI :
10.1109/BIBE.2008.4696845
Filename :
4696845
Link To Document :
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